融合鲸鱼算法的混合灰狼优化算法

针对灰狼优化(GWO)算法存在容易陷入局部最优、收敛速度慢、求解精度不高等问题,提出一种融合鲸鱼算法的混合灰狼优化(HWGWO)算法.首先在鲸鱼算法的螺旋泡网狩猎行为中融入Levy飞行并将其整体引入灰狼优化算法;然后将动态权重和差分进化思想引入灰狼优化算法;最后利用贪婪选择策略来保留较好的灰狼位置.选取23个测试函数进行数值试验,结果表明,HWGWO算法在收敛速度和求解精度上都有所提升.此外,利用HWGWO算法求解拉伸/压缩弹簧设计问题得到的设计方案更有效.     

基于正弦控制因子的Lateral变异鲨鱼优化算法

针对传统鲨鱼优化算法在求解高维目标函数时,易早熟收敛,陷入局部最优的缺陷.提出一种基于正弦控制因子的Lateral变异鲨鱼优化算法.通过正弦曲线的特性和自适应惯性权重,改善了传统鲨鱼优化算法中由于随机选取控制因子数值大小可能导致算法在迭代后期全局搜索能力降低的问题,提高了算法在迭代后期的全局收敛能力,并对最佳鲨鱼位置引入Lateral变异策略,加强了算法跳出局部最优的可能性.改进后的算法对多个shifted单峰,多峰以及固定维测试函数进行求解,实验结果表明,对比多种不同优化算法而言,本文所提LSSO算法具有更高的收敛精度和搜索速度.     

三层前向人工神经网络全局最优逼近

提出了求解不等式约束非线性优化问题的群体复合形进化算法 ,提出的算法能充分利用目标函数值的信息、优化搜索过程具有较强的方向性和目标性 ,收敛速度较快 ,且是全局优化算法 ;将群体复合形进化算法应用于三层前向人工神经网络逼近 ,提出了三层前向人工神经网络全局最优逼近算法 ;将三层前向人工神经网络全局最优逼近算法应用于实例 ,表明了提出的全局最优逼近算法的有效性 .     

组合优化算法中摆脱局部极小值的几种策略

局部搜索算法是一种非常有效的求解组合优化问题的算法,它具有通用、灵活等特点。但是,由于搜索空间和目标函数的复杂性,目标函数在搜索空间中有许多局部极小值点,使算法在这些局部极小值点处被“卡住”,大大影响算法的效果。对于此问题,笔查阅了大量献资料,结合自己的研究实践,总结出几种跳出局部极小“陷井”的策略,使用这些策略,有望使算法更加完善,在求解组合优化问题过程中更能发挥其作用。     

一个新的无约束优化超记忆梯度算法

本文提出一种新的无约束优化超记忆梯度算法,算法利用当前点的负梯度和前一点的负梯度的线性组合为搜索方向,以精确线性搜索和Armijo搜索确定步长．在很弱的条件下证明了算法具有全局收敛性和线性收敛速度．因算法中避免了存贮和计算与目标函数相关的矩阵,故适于求解大型无约束优化问题．数值实验表明算法比一般的共轭梯度算法有效．     

基于引力搜索和粒子群混合优化算法的证券投资组合问题研究

本文研究考虑交易成本的投资组合模型,分别以风险价值(VAR)和夏普比率(SR)作为投资组合的风险评价指标和效益评价指标。为有效求解此模型,本文在引力搜索和粒子群算法的基础上提出了一种混合优化算法(IN-GSA-PSO),将粒子群算法的群体最佳位置和个体最佳位置与引力搜索算法的加速度算子有机结合,使混合优化算法充分发挥单一算法的开采能力和探索能力。通过对算法相关参数的合理设置,算法能够达到全局搜索和局部搜索的平衡,快速收敛到模型的最优解。本文选取上证50股2014年下半年126个交易日的数据,运用Matlab软件进行仿真实验,实验结果显示,考虑交易成本的投资组合模型可使投资者得到更高的收益率。研究同时表明,基于PSO和GSA的混合算法在求解投资组合模型时比单一算法具有更好的性能,能够得到满意的优化结果。     

LC^1类优化问题的Broyden算法的收敛性分析

本文讨论了利用Ｂｒｏｙｄｅｎ算法求解ＬＣ＾１类优化问题。所谓ＬＣ＾１类优化问题是指目标函数一阶连续可微，且它的导函数Ｌｉｐｓｃｈｉｔｚ连续。若假设目标函数是强凸的，讨论了该算法的全局收敛性；若假设目标函数是半光滑的，还给出了该算法的超线性收敛性分析。     

LMI优化的一种原对偶中心路径算法

系统和控制理论中许多重要的问题,都可转化为具有线性目标函数、线性矩阵不等式约束的LMI优化问题,从而使其在数值上易于求解.本文给出一种求解LMI优化问题的原对偶中心路径算法,该算法利用牛顿方法求解中心路径方程得到牛顿系统,并将该牛顿系统对称化以避免得到非对称化的搜索方向.文章详细分析了算法的计算复杂性.     

热传导(对流-扩散)方程源项识别的粒子群优化算法

提出了利用粒子群优化(PSO)算法反演热传导方程与对流-扩散方程源项的一种新方法,在已有文献方法的基础上,求解出这两类方程正问题的解析解,再把源项识别问题转化为最优化问题,结合粒子群优化算法寻优求解.通过数值模拟与统计检验,结果表明,此方法可快速有效地实现热传导方程与对流-扩散方程源项的识别,并可推广应用到其它数学物理方程的源项或参数的反演识别.     

基于人机交互—蚁群算法的港口疏船调度优化

为解决因港口无法正常作业导致大量船舶压港后的疏船调度问题,从同时兼顾船公司和港口方利益出发,建立了船舶平均在港时间最短、额外作业成本最低、生产秩序恢复最快的调度生产多目标优化模型。利用多属性效用理论将多目标转换为单目标,并构建了相应的评价函数,采用改进的蚁群算法并结合人机交互以及邻域搜索方法求解,最后以大连港集装箱码头实际案例进行验证。结果表明,与通常调度方法相比,文中建立的优化模型能够更好地解决疏船问题;对比常规的蚁群算法,改进后的算法搜索效率更高。上述模型和算法为集装箱码头的生产组织调度提供了新的优化思路和方法。     

Identification of Pipeline Inner Wall Geometry Based on the POD⁃RBF Method北大核心CSCD

Based on the proper orthogonal decomposition⁃radial basis function (POD⁃RBF), a geometric identification method for pipeline inner wall was proposed to solve the internal corrosion detection problem of natural gas and oil pipelines. In view of the static magnetic field, the simplified finite element model for the pipelines was established, and the variable⁃geometry sample library was constructed, to realize the response prediction of arbitrary geometry by the POD⁃RBF. The proposed method achieves reduced⁃order analysis and avoids repeated solution of the stiffness matrix due to the geometrical change during the identification process. Hence, it can significantly improve the computation efficiency. Finally, the grey wolf optimization (GWO) algorithm was used to optimize the objective function and avoid the calculation of the sensitivity in the process of geometry change. The numerical examples show that, the proposed method has high efficiency and accuracy in the geometric identification of the pipeline inner wall, with good stability even under introduced noises. © 2023 Editorial Office of Applied Mathematics and Mechanics. All rights reserved.     

Inspired grey wolf optimizer for solving large-scale function optimization problems

Grey wolf optimizer algorithm was recently presented as a new heuristic search algorithm with satisfactory results in real-valued and binary encoded optimization problems that are categorized in swarm intelligence optimization techniques. This algorithm is more effective than some conventional population-based algorithms, such as particle swarm optimization, differential evolution and gravitational search algorithm. Some grey wolf optimizer variants were developed by researchers to improve the performance of the basic grey wolf optimizer algorithm. Inspired by particle swarm optimization algorithm, this study investigates the performance of a new algorithm called Inspired grey wolf optimizer which extends the original grey wolf optimizer by adding two features, namely, a nonlinear adjustment strategy of the control parameter, and a modified position-updating equation based on the personal historical best position and the global best position. Experiments are performed on four classical high-dimensional benchmark functions, four test functions proposed in the IEEE Congress on Evolutionary Computation 2005 special session, three well-known engineering design problems, and one real-world problem. The results show that the proposed algorithm can find more accurate solutions and has higher convergence rate and less number of fitness function evaluations than the other compared techniques.     

一类不可微二次规划逆问题

本文求解了一类二次规划的逆问题,具体为目标函数是矩阵谱范数与向量无穷范数之和的最小化问题.首先将该问题转化为目标函数可分离变量的凸优化问题,提出用G-ADMM法求解.并结合奇异值阈值算法,Moreau-Yosida正则化算法,matlab优化工具箱的quadprog函数来精确求解相应的子问题.而对于其中一个子问题的精确求解过程中发现其仍是目标函数可分离变量的凸优化问题,由于其变量都是矩阵,所以采用适合多个矩阵变量的交替方向法求解,通过引入新的变量,使其每个子问题的解都具有显示表达式.最后给出采用的G-ADMM法求解本文问题的数值实验.数据表明,本文所采用的方法能够高效快速地解决该二次规划逆问题.     

考虑共享不确定因素的应急设施最大覆盖选址模型

为提升应急设施的服务质量和抵御中断风险的能力,研究应急设施最大覆盖选址-分配决策问题。扩展无容量限制的固定费用的可靠性选址决策模型,建立考虑共享不确定因素的应急设施最大覆盖选址优化模型,通过在目标和约束中引入budget不确定集刻画共享不确定因素,基于Bertsimas和Sim鲁棒优化方法建立混合整数规划模型,并将非线性问题转化为易于求解的鲁棒等价模型,利用带混沌搜索策略的改进灰狼优化算法求解模型,并对不确定鲁棒水平和中断概率进行敏感性分析。最后通过案例及数据仿真结果的对比分析,验证了模型的合理性和有效性,并给出最优的选址分配布局。     

Degree reduction of SG-Bézier surfaces based on grey wolf optimizer

Aiming at the problem of approximate degree reduction of SG-Bézier surfaces, a method is proposed to achieve the degree reduction from (n × n) to (m × m) (m < n). Starting from the idea of grey wolf optimizer (GWO) algorithm and combining the geometric properties of SG-Bézier surfaces, this method transforms the degree reduction problem of SG-Bézier surfaces into an optimization problem. By choosing the fitness function, the degree reduction approximation of shape-adjustable SG-Bézier surfaces under unconstrained and angular interpolation constraints is realized. At the same time, some concrete examples of degree reduction and its errors are given. The results show that this method not only achieves good degree reduction effect but also is easy to implement and has high precision.     

投资者偏好条件下概率准则投资组合问题求解

投资者偏好条件下概率准则投资组合问题是经典投资组合问题的深化,是在非负投资比例系数约束条件下,以概率准则和偏好因子为目标函数的优化模型。设计了一种基于万有引力搜索算法的求解方法,个体的质量对应于目标函数值,位置对应于投资比例系数,结合速度和位置更新策略,建立了算法的求解步骤。通过数值实验和与现有解法的比较,验证了算法的可行性和有效性。     

Pareto memetic algorithm for multiple objective optimization with an industrial application

Multiple objective combinatorial optimization problems are difficult to solve and often, exact algorithms are unable to produce optimal solutions. The development of multiple objective heuristics was inspired by the need to quickly produce acceptable solutions. In this paper, we present a new multiple objective Pareto memetic algorithm called PMS^MO. The PMS^MO algorithm incorporates an enhanced fine-grained fitness assignment, a double level archiving process and a local search procedure to improve performance. The performance of PMS^MO is benchmarked against state-of-the-art algorithms using 0–1 multi-dimensional multiple objective knapsack problem from the literature and an industrial scheduling problem from the aluminum industry.     

An algorithm for solving global optimization problems with nonlinear constraints

In this paper we propose an algorithm using only the values of the objective function and constraints for solving one-dimensional global optimization problems where both the objective function and constraints are Lipschitzean and nonlinear. The constrained problem is reduced to an unconstrained one by the index scheme. To solve the reduced problem a new method with local tuning on the behavior of the objective function and constraints over different sectors of the search region is proposed. Sufficient conditions of global convergence are established. We also present results of some numerical experiments.     

基于部件指派的多状态系统可靠度优化研究

针对一般二态系统假设的不足,提出了多状态系统条件下的可靠度优化指派问题。该问题以系统可靠度最大化为优化目标,在考虑部件分配成本和总分派成本预算的前提下,对多状态系统下不同状态对应的性能水平的进行了分析,给出了基于通用生成函数的多状态系统的可靠度评估方法。根据指派问题的组合优化的特性和多状态系统可靠性评估的特点,对传统遗传算法的适应度函数进行了改进,设计了基于整数编码的遗传算法,该算法具有离散变量的设计灵活性和强大的搜索性能。算例实验表明,本文设计的优化算法具有较好的求解质量,同时算法的运行时间也得到了大幅的缩短。本研究为多状态系统的可靠度优化提供了一条可借鉴的思路。     

A genetic algorithm for a global optimization problem arising in the detection of gravitational waves

The detection of gravitational waves is a long-awaited event in modern physics and, to achieve this challenging goal, detectors with high sensitivity are being used or are under development. In order to extract gravitational signals emitted by coalescing binary systems of compact objects (neutron stars and/or black holes), from noisy data obtained by interferometric detectors, the matched filter technique is generally used. Its computational kernel is a box-constrained global optimization problem with many local solutions and a highly nonlinear and expensive objective function, whose derivatives are not available. To tackle this problem, we designed a real-coded genetic algorithm that exploits characteristic features of the problem itself; special attention was devoted to the choice of the initial population and of the recombination operator. Computational experiments showed that our algorithm is able to compute a reasonably accurate solution of the optimization problem, requiring a much smaller number of function evaluations than the grid search, which is generally used to solve this problem. Furthermore, the genetic algorithm largely outperforms other global optimization algorithms on significant instances of the problem.